This invention relates generally to computer systems and specifically to systems which can assimilate current activities into its memory.
Ever since the computer was first developed, it has been touted as a "thinking" device. In fact, early hesitancy to use computers was the result of fear that the computer could out-think its owner.
As the use of computers spread, the magical attributes of the computer were debunked. It was soon recognized that the computer was only as smart as the programmer. The computer was a tool that could be used only for very definite tasks; and, these tasks had to be completely understood by the programmer.
The need for a machine which could do more than number crunch or manipulate verbiage became more pronounce and a variety of rudimentary skills were subsequently incorporated into the computer. One of these skills included Computer Aided Engineering (CAE). Although computers which had this ability could perform some rudimentary engineering (i.e. find an efficient connection path between components), the computers were still only as intelligent as the designer. That is, the machine was no more efficient after years of operation than it was on its first day. The experience which it had gained was not stored, analyzed, or assimilated.
Presently some more work has been performed in creating "Expert Systems". These are computer systems which are designed to perform a particular function such as the legal practice of drafting a will or medical diagnosis. As with CAE though, the "Expert System" does not gain or improve upon itself with added experience without human intervention into its underlying principals and knowledge base.
Still another concept which has been used is described in the paper entitled "Hierarchy in Sequential and Concurrent Systems", by Michael Manthey, and published by the Department of Computer Science at the University of New Mexico.
In the Manthey approach, an entire universe of knowledge concerning the physical world's structure and interaction is formed into a directed graph of nodes and arcs. Each node is representative of an action, the arcs are representative of the "leadsto" relation. That is, the arcs define the sequence with which the actions occur. Using a special algorithm, the computer then identifies cycles in the directed graph and collapses the chosen cycle into a single nodal representation. This new composite node is in essence a summary of the cycle.
In this approach though, knowledge of the entire universe for the system must be given a priori. That is, all knowledge about all possible actions and their interrelationships must be known before any computer extraction or generalization may be done. In the real world, this idyllic situation does not exist since knowledge about the system is gathered piecemeal.
It is clear from the foregoing that a need does exist for a system which can learn through experience; what is needed is a system that can collate data into the best knowledge available at the time. This knowledge then could assist the human operator in evaluation of the environment or assist another device in its activities.